Decomposing spatio-temporal seismicity patterns

Abstract

Abstract. Seismicity is a distributed process of great spatial and temporal variability and complexity. Efforts to characterise and describe the evolution of seismicity patterns have a long history. Today, the detection of changes in the spatial distribution of seismicity is still regarded as one of the most important approaches in monitoring and understanding seismicity. The problem of how to best describe these spatiotemporal changes remains, also in view of the detection of possible precursors for large earthquakes. In particular, it is difficult to separate the superimposed effects of different origin and to unveil the subtle (precursory) effects in the presence of stronger but irrelevant constituents. I present an approach to the latter two problems which relies on the Principal Components Analysis (PCA), a method based on eigenstructure analysis, by taking a time series approach and separating the seismicity rate patterns into a background component and components of change. I show a sample application to the Southern California area and discuss the promising results in view of their implications, potential applications and with respect to their possible precursory qualities.

Configurational entropy of critical earthquake populations

“…The sensitivity of H ** to clustering naturally leads to the question if our approach might be useful in achieving the largely unreached goal of separating correlated from uncorrelated earthquakes. As all conventional declustering methods require the definition of internal parameters as e.g., pre‐defined space‐time windows [e.g., Goltz , 2001] our approach has the clear advantage of no internal parameters at all. We therefore propose to include configurational entropy in the process of earthquake declustering, at least for testing the results of other methods, and will adress this issue in a future paper.…”

Earthquake recurrence as a record breaking process

“…Recently, such an approach has brought to light new properties of the clustering of seismicity in space and time [ Bak et al , 2002; Corral , 2003, 2004; Davidsen and Goltz , 2004; Davidsen and Paczuski , 2005; Baiesi and Paczuski , 2005], which can potentially be exploited for earthquake prediction [ Goltz , 2001; Tiampo et al , 2002; Baiesi , 2006]. One aim has been to evaluate distances between subsequent events, including temporal and spatial measures.…”

Extracting borehole strain precursors associated with the Lushan earthquake through principal component analysis

“…Furthermore, Lin [2011] used PCA and image processing to identify ionospheric total electron content anomalies before a strong earthquake. C. Goltz [2001] used PCA to study the seismicity of the Southern California area and discussed the promising results in view of their implications, potential applications and possible precursory qualities. Fang Y et al [2015] identified possible short-and medium-term earthquake precursors before the Lushan earthquake by using PCA to investigate the cross-fault data.…”